





'''simple docstring'''




import math
import random
from typing import Any

from .hill_climbing import SearchProblem





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Union[str, Any]      ,    __lowerCAmelCase       :							bool = True      ,    __lowerCAmelCase       :							float = math.inf      ,    __lowerCAmelCase       :							float = -math.inf      ,    __lowerCAmelCase       :							float = math.inf      ,    __lowerCAmelCase       :							float = -math.inf      ,    __lowerCAmelCase       :							bool = False      ,    __lowerCAmelCase       :							float = 1_00      ,    __lowerCAmelCase       :							float = 0.01      ,    __lowerCAmelCase       :							float = 1      ,    )		->  Union[str, Any]:
   snake_case 						=   False
   snake_case 						=   search_prob
   snake_case 						=   start_temperate
   snake_case 						=   []
   snake_case 						=   0
   snake_case 						=   None

   while not search_end:
      snake_case 						=   current_state.score()
      if best_state is None or current_score > best_state.score():
         snake_case 						=   current_state
      scores.append(a_		)
      iterations += 1
      snake_case 						=   None
      snake_case 						=   current_state.get_neighbors()
      while (
          next_state is None and neighbors
      ):  # till we do not find a neighbor that we can move to
         snake_case 						=   random.randint(0      ,    len(a_		) - 1		)  # picking a random neighbor
         snake_case 						=   neighbors.pop(a_		)
         snake_case 						=   picked_neighbor.score() - current_score

         if (
             picked_neighbor.x > max_x
             or picked_neighbor.x < min_x
             or picked_neighbor.y > max_y
             or picked_neighbor.y < min_y
         ):
            continue  # neighbor outside our bounds

         if not find_max:
            snake_case 						=   change * -1  # in case we are finding minimum
         if change > 0:  # improves the solution
            snake_case 						=   picked_neighbor
         else:
            snake_case 						=   (math.e) ** (
                change / current_temp
            )  # probability generation function
            if random.random() < probability:  # random number within probability
               snake_case 						=   picked_neighbor
      snake_case 						=   current_temp - (current_temp * rate_of_decrease)

      if current_temp < threshold_temp or next_state is None:
         # temperature below threshold, or could not find a suitable neighbor
         snake_case 						=   True
      else:
         snake_case 						=   next_state

   if visualization:
      from matplotlib import pyplot as plt

      plt.plot(range(a_		)      ,    a_		)
      plt.xlabel("""Iterations"""		)
      plt.ylabel("""Function values"""		)
      plt.show()
   return best_state


if __name__ == "__main__":





 def 	__lowerCamelCase			(      __lowerCAmelCase       :							Tuple      ,    __lowerCAmelCase       :							Union[str, Any]		)		->  Union[str, Any]:
    return (x**2) + (y**2)

 # starting the problem with initial coordinates (12, 47)
 _SCREAMING_SNAKE_CASE							     =			SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 _SCREAMING_SNAKE_CASE							     =			simulated_annealing(
     prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
 )
 print(
     "The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
     F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
 )

 # starting the problem with initial coordinates (12, 47)
 _SCREAMING_SNAKE_CASE							     =			SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 _SCREAMING_SNAKE_CASE							     =			simulated_annealing(
     prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
 )
 print(
     "The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
     F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
 )





 def 	__lowerCamelCase			(      __lowerCAmelCase       :							Union[str, Any]      ,    __lowerCAmelCase       :							str		)		->  Union[str, Any]:
    return (3 * x**2) - (6 * y)

 _SCREAMING_SNAKE_CASE							     =			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 _SCREAMING_SNAKE_CASE							     =			simulated_annealing(prob, find_max=False, visualization=True)
 print(
     "The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
     F"""{local_min.score()}"""
 )

 _SCREAMING_SNAKE_CASE							     =			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 _SCREAMING_SNAKE_CASE							     =			simulated_annealing(prob, find_max=True, visualization=True)


 print(
     "The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
     F"""{local_min.score()}"""
 )







'''simple docstring'''


from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

UpperCAmelCase_					=   {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

UpperCAmelCase_					=   {
    "facebook/blenderbot_small-90M": 5_12,
}





class  __lowercase    (	__magic_name__			):
   _a    						=   VOCAB_FILES_NAMES
   _a    						=   PRETRAINED_VOCAB_FILES_MAP
   _a    						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a    						=   BlenderbotSmallTokenizer







   def __init__(						self   ,				UpperCamelCase=None   ,				UpperCamelCase=None   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase=False   ,				UpperCamelCase=True   ,				**UpperCamelCase   ,				)					->     List[Any]:
     super().__init__(
         ByteLevelBPETokenizer(
             vocab=UpperCamelCase   ,				merges=UpperCamelCase   ,				add_prefix_space=UpperCamelCase   ,				trim_offsets=UpperCamelCase   ,				)   ,				bos_token=UpperCamelCase   ,				eos_token=UpperCamelCase   ,				unk_token=UpperCamelCase   ,				**UpperCamelCase   ,				)
     __a								=       add_prefix_space







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase=None   )					->     List[Any]:
     __a								=       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
       return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = None   )					->     List[int]:
     __a								=       [self.sep_token_id]
     __a								=       [self.cls_token_id]

     if token_ids_a is None:
       return len(cls + token_ids_a + sep   ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]



